Automating the expert consensus paradigm for robust lung tissue classification

Srinivasan Rajagopalan, Ronald A. Karwoski, Sushravya Raghunath, Brian Jack Bartholmai, Richard A. Robb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8315
DOIs
StatePublished - 2012
EventMedical Imaging 2012: Computer-Aided Diagnosis - San Diego, CA, United States
Duration: Feb 7 2012Feb 9 2012

Other

OtherMedical Imaging 2012: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego, CA
Period2/7/122/9/12

Fingerprint

Computerized tomography
lungs
Workflow
Tissue
Lung
Pulmonary diseases
Probability density function
Automation
Agglomeration
Lung Diseases
physicians
Physicians
void ratio
automation
probability density functions
partitions
interactions

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Rajagopalan, S., Karwoski, R. A., Raghunath, S., Bartholmai, B. J., & Robb, R. A. (2012). Automating the expert consensus paradigm for robust lung tissue classification. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 8315). [831530] https://doi.org/10.1117/12.912009

Automating the expert consensus paradigm for robust lung tissue classification. / Rajagopalan, Srinivasan; Karwoski, Ronald A.; Raghunath, Sushravya; Bartholmai, Brian Jack; Robb, Richard A.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8315 2012. 831530.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rajagopalan, S, Karwoski, RA, Raghunath, S, Bartholmai, BJ & Robb, RA 2012, Automating the expert consensus paradigm for robust lung tissue classification. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 8315, 831530, Medical Imaging 2012: Computer-Aided Diagnosis, San Diego, CA, United States, 2/7/12. https://doi.org/10.1117/12.912009
Rajagopalan S, Karwoski RA, Raghunath S, Bartholmai BJ, Robb RA. Automating the expert consensus paradigm for robust lung tissue classification. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8315. 2012. 831530 https://doi.org/10.1117/12.912009
Rajagopalan, Srinivasan ; Karwoski, Ronald A. ; Raghunath, Sushravya ; Bartholmai, Brian Jack ; Robb, Richard A. / Automating the expert consensus paradigm for robust lung tissue classification. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 8315 2012.
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